a year ago
research-and-dataA lightweight, pure web search solution for large language models, supporting multi-engine aggregated search, deep reflection and result evaluation. A balanced approach between web search and deep research, providing a framework-free implementation and mcp server for easy developer integration.
Overview
What is Deep Search Lighting?
Deep Search Lighting is a lightweight web search solution designed for large language models, enabling multi-engine aggregated search, deep reflection, and result evaluation. It offers a balanced approach between web search and deep research, with a framework-free implementation for easy integration by developers.
How to use Deep Search Lighting?
To use Deep Search Lighting, follow these steps:
- Install the package using conda and pip.
- Configure your model information in the .env file.
- Run the demo or the MCP server using the provided Python scripts.
Key features of Deep Search Lighting?
- Multi-engine aggregated search (supports Baidu, DuckDuckGo, Bocha, Tavily)
- Reflection strategies for model self-evaluation
- Custom pipelines compatible with all LLM models
- OpenAI-style API compatibility
- Built-in MCP server support for easy integration
Use cases of Deep Search Lighting?
- Enhancing search capabilities for language models.
- Evaluating search results through reflection mechanisms.
- Integrating with various APIs for improved query quality.
FAQ from Deep Search Lighting?
- What search engines does Deep Search Lighting support?
It supports Baidu, DuckDuckGo, Bocha, and Tavily.
- Is there a cost associated with using Deep Search Lighting?
No, it works with free APIs while maintaining good query quality.
- Can it be used with smaller language models?
Yes, it supports models of any size, including smaller ones.